Publication: Hybrid sequence learning with interpretability for multi-class quality prediction in injection molding
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Issued Date
2025-09-01
Resource Type
eISSN
25901230
Scopus ID
2-s2.0-105011758694
Journal Title
Results in Engineering
Volume
27
Rights Holder(s)
SCOPUS
Bibliographic Citation
Results in Engineering Vol.27 (2025)
Suggested Citation
Punyangarm V., Chotayakul S. Hybrid sequence learning with interpretability for multi-class quality prediction in injection molding. Results in Engineering Vol.27 (2025). doi:10.1016/j.rineng.2025.106408 Retrieved from: https://hdl.handle.net/20.500.14740/21228
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Author's Affiliation
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Abstract
Ensuring consistent quality in injection molding remains a critical challenge due to dynamic process variations and the limitations of traditional rule-based inspection methods. This study proposes a novel hybrid deep learning framework that integrates a Transformer encoder with a TabNet classifier to enable interpretable, multi-class defect prediction using time-series part weight data. The Transformer module captures long-range temporal dependencies, while TabNet provides feature-level interpretability through sparse attention masks. The model was trained and validated on real-world data from over 30,000 injection cycles, covering five classes: acceptable part, short shot, flash, sink mark, and warpage. Evaluation results demonstrate that the proposed model significantly outperforms conventional machine learning methods such as Random Forest, XGBoost, CatBoost, and a hybrid deep learning baseline (CNN–TabNet), achieving a macro F1-score of 0.964 and a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.992. It also maintains high robustness under signal noise and supports inference within 100 milliseconds, enabling near real-time deployment (i.e., high-speed analysis of recent production windows). Importantly, the model offers actionable insights through built-in explainability mechanisms, helping operators understand and trace the root causes of predicted defects. This research contributes a scalable, low-cost, and interpretable solution for proactive quality monitoring, paving the way for practical adoption of explainable AI in smart manufacturing environments.